Wireless communication systems, such as orthogonal frequency division multiplexing (OFDM) communication systems, are based on a physical layer structure in which frequency axis and time axis resources are allocated and used for data transmission. In such wireless communication systems, a transmitter two-dimensionally places reference signal (RS) symbols, such as pilot symbols, in resource elements (REs) within a resource block (RB) to increase channel estimation performance and then transmits the RS symbols. A receiver performs channel estimation on the basis of the RS symbols placed in the REs to enhance the accuracy for the channel estimation.
Meanwhile, a time-domain/frequency-domain filtering method, an interpolation method, a least square (LS) method, a minimum mean square error (MMSE) method, a 2-dimensional Wiener filtering method, and the like are widely used as a method for channel estimation. The above channel estimation methods may be selected and used on the basis of a wireless channel environment. That is because channel estimation performance varies with the channel estimation method that is used in the corresponding wireless channel environment. Accordingly, an appropriate channel estimation method may be selected and used according to whether the wireless channel environment is a frequency selective fading environment, a flat fading environment, or an environment in which user equipment (UE) moves at a high or low speed.
In general, when a two-dimensional statistical characteristic (Wide-sense stationary uncorrelated scattering: WSSUS) of a channel is known, the 2-dimensional Wiener filtering method is most effective for channel estimation and is widely used in the actual implementation. However, in cases where the 2-dimensional Wiener filtering method is used in multiple input multiple output (MIMO) communication systems, channel estimation performance is degraded due to interference. That is, in the MIMO communication systems, interference is not completely removed even though an interference removal operation is performed by distinguishing between signals of respective UEs, and therefore the channel estimation performance is affected by the unremoved interference. That is because the existing 2-dimensional Wiener filtering method of performing channel estimation by applying a Wiener filter to a single layer signal does not consider interference existing after signal separation for multiple layer signals.